aiqbench analysis
This paper tackles a practical bottleneck in quantum algorithms: loading classical multivariable functions into quantum states, which often dominates circuit depth. The proposed shallow QROM and state-preparation oracles reduce depth by roughly a square root in the qubit count, but only by spending exponentially many ancilla qubits, a trade-off that will determine whether the approach is useful on near-term hardware. Readers should watch for benchmarks on concrete algorithms and resource estimates that account for the ancilla overhead.
Shallow data-loading circuits for multivariable functions
arXiv:2611.02296v1 Announce Type: new Abstract: We construct shallow QROM, diagonal block-encoding, and quantum state preparation oracles for analytic functions on the $D$-dimensional hypercube $[0, 1]^D$. On $n$ qubits and with $2^{- \Omega (n/D)}$ error, these circuits have depths of $O \left( \sqrt{\frac{n \log_2 (n)}{D}} \right)$ in the Clifford + $T$ gate set, albeit using $2^{O(\sqrt{nD \log_2 (n)})}$ ancilla qubits. This is a near-quadratic depth reduction compared to known linear-depth methods.
Abstract:We construct shallow QROM, diagonal block-encoding, and quantum state preparation oracles for analytic functions on the $D$-dimensional hypercube $[0, 1]^D$. On $n$ qubits and with $2^{- \Omega (n/D)}$ error, these circuits have depths of $O \left( \sqrt{\frac{n \log_2 (n)}{D}} \right)$ in the Clifford + $T$ gate set, albeit using $2^{O(\sqrt{nD \log_2 (n)})}$ ancilla qubits. This is a near-quadratic depth reduction compared to known linear-depth methods.
18 pages; comments are welcome!
arXiv:2611.02296 [quant-ph]
(or arXiv:2611.02296v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.02296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 161 words